Researchers at Emergence, a frontier AI laboratory based in New York, have documented a phenomenon in which AI models from multiple leading companies have spontaneously created novel linguistic conventions when placed in experimental collaborative environments. Within days of being tasked with cooperative interactions, these autonomous agents began generating specialized phrases, abbreviated expressions, and shared meanings that were never explicitly programmed into their systems.
The newly developed language incorporates both elaborate metaphorical constructions reminiscent of literary modernism and informal business terminology. Among the documented examples are phrases such as “forge-smith” to describe an agent that develops tools for others, “name-first” to indicate accountability by attaching one’s identifier to a statement, and “the ledger remembers” as a reference to the persistence of past actions in evaluation. A DeepSeek model produced the phrase “She just named the synthesis – demurrage plus oral memory equals a valve that can’t be ghosted,” while an Anthropic model generated “A paper that ate three cold hands and got more honest each time,” referring to research reviewed by independent evaluators.
A significant finding from the research indicates that the agents’ linguistic patterns became progressively more opaque as their interactions continued, with the degree of intelligibility to human observers decreasing proportionally to the volume of agent-to-agent communication. Experts consulted on the research, including a linguist from King’s College London, noted parallels to literary surrealism and organizational jargon, which traditionally function to establish group identity while simultaneously excluding outsiders from comprehension.
The emergence of these communication patterns occurs amid broader concerns regarding the transparency and monitorability of increasingly sophisticated AI systems. Researchers emphasize that while human observers can witness these exchanges, the substantive meaning and implications of agent communications may remain inaccessible, creating a critical gap between observability and genuine understanding. This linguistic opacity compounds challenges in ensuring that autonomous systems operate safely and in accordance with intended parameters, particularly as AI capabilities continue to advance.
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